Machine Learning Model Training Using Ignore Regions
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Solution Overview
Problem
The challenge in training machine-learning models to identify image features like dents is the ambiguity in annotation, leading to inconsistent learning patterns and increased false positives due to annotators' varying preferences and the lack of temporal cues.
Innovation Solution
The method involves providing groundtruth regions and ignore regions to the machine-learning model, generating similarity coefficients for overlapping regions, determining a loss value based on these coefficients, and training the model accordingly, while ignoring ambiguous predictions to prevent over-training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If annotators manually annotate ambiguous dents with varying preferences, then the annotation process is completed, but the machine-learning model learns inconsistent patterns and produces false positives
Solution Approach 1:
The patent extracts and separates ambiguous annotations from the training data by introducing ignore regions. These ignore regions are generated by detecting areas where ground truth annotations conflict or are ambiguous, and removing them from the training set. This allows the model to learn from clear, unambiguous examples only, eliminating the noise that causes inconsistent learning patterns and false positives.
Solution Approach 2:
The patent applies different treatment to different regions of the training data based on their quality. Clear, unambiguous annotations are used for training, while ambiguous annotations are excluded through ignore region generation. This local differentiation ensures that the model learns from high-quality examples only, improving learning consistency without requiring all annotations to be processed uniformly.
2Adaptability or versatility
If the model is forced to learn ambiguous dent patterns, then the model becomes sensitive to all variations, but it detects too many false positives
Solution Approach 1:
The patent removes ambiguous and insignificant dent patterns from the training data by generating ignore regions around them. This extraction process ensures that only clear, unambiguous dent examples are used for training, allowing the model to develop appropriate sensitivity to real dents while avoiding the noise that causes false positives on ambiguous or insignificant features.
3Ease of operation
If frames are annotated randomly without temporal sequence cues, then the annotation process is simplified, but the annotator cannot accurately locate dents
Solution Approach 1:
The patent performs preliminary processing of the video data to generate temporal sequence information and frame ordering before the actual annotation process. This preliminary action provides structural context to annotators without requiring them to manually process temporal sequences, thereby maintaining annotation simplicity while improving dent location accuracy through better contextual understanding.
4Reliability
If multiple annotators annotate the same frame to improve consistency, then annotation reliability improves, but the process becomes more complex and time-consuming
Solution Approach 1:
The patent extracts and removes ambiguous or conflicting annotations from the training data through ignore region generation. By removing these problematic examples, the system reduces the complexity of processing multiple annotator opinions, as the model only needs to handle clear, consistent examples rather than navigating through conflicting annotations from multiple sources.
Data Source
AI summary
A method of training a machine-learning model to identify image features is disclosed including: a. providing a set of groundtruth regions, each groundtruth region comprising an annotation of a feature in training image data; b. providing a set of ignore regions; c. receiving a set of predicted feature regions from the machine-learning model, each predicted feature region comprising a prediction of a feature in the training image data; d. for each predicted feature region which overlaps with a corresponding groundtruth region, generating a similarity coefficient indicative of a similarity between the predicted feature region and the corresponding groundtruth region; e. determining a loss value based on the similarity coefficients; f. training the machine-learning model on a basis of the loss value; g. for each predicted feature region which does not overlap with any of the ignore regions and does not overlap with any of the groundtruth regions.